Weibull plots provide graphical
input to help define clearly the difference between common cause variability
and special cause variability.Exception
reports or variance reports in production facilities are generally directed
toward explaining special causes which have names for the deficiencies in
production output.Details are explained
in the October 2008 problem.Figure 1 shows a clear example of special
causes separated from common causes with the cusp designating the reliability
of the process.

Figure 1: Separation Of Variability By Special Causes And Common Causes

For Figure 1: ●the
cusp at 91% separates the steep trendline of common cause variability from the special cause variability with a
shallow trendline●special cause losses (the sum of all gaps between the data points and
the production trendline below 91%) are 10,184
tons/year, ●common cause losses (the sum of all gaps between the nameplate line and
the production line) are 83,133 tons/year
(don’t be trapped by the logarithmic X-axis scale) which totals to a hidden
factory of 93,316 tons/ year.Next, monetize the losses so you speak the
language of business.

Suppose for Figure 1 the gross
margin $’s are $500/ton:●special cause losses are $5,092,000/year,ßProblem #2●common cause losses are $41,566,500/year,ßProblem #1●total
hidden factory loss is $46,658,000/year problem—this is not a chicken feed issue!The problem order for solution is based on the Pareto
distribution.
Notice that 10.9% of losses are due to special cause and 89.1% of losses are
due to common causes.The annual losses
are equivalent to 93 days of production not achieved!Compare the % losses to Demings
numbers below which are based on his experience.

Most accounting departments will
tell you about the $46,658,000 problem, however, they will not usually be able
to tell you the specific items needed for correcting the losses nor will they
define the skills need for corrective action.The Weibull plot tells the special cause losses need to be corrected by
the reliability and maintenance departments.The Weibull plot also tells the common cause losses are squarely in the
hands of the black belt problem solvers and directly in the hands of management
to resolve.

For Figure 1, the Pareto
distribution sets the work priority as first solving the common cause losses
and second on solving the special cause losses.Since different skills sets are required, the problems can be approached
simultaneously.

For the plant in Figure 1, the
original emphasis of the study was on reducing maintenance costs (a typical scapegoat) for the special
causes which is not the most important medicine for this facility.The real issue in dire need solving first is
reducing production variability to reduce common cause variability.If the campaign to reduce common cause was
50% effective and reduced common cause variability losses to 41,000 tons/year
and this would have produced a savings 4 times larger than reducing all special
cause events!The point is obvious: you
must know where to attack the problem and the real problem may not be your
typical scapegoat.

The adage: If you’re a carpenter,
then most of your problems must look like a nail applies to our skill
sets:●if
you are a maintenance guru, all your problems will appear as reducing maintenance efforts (the small portion of
the data set in Figure 1),●if
you’re a master black belt problem solver all your problems will appear as reducing variability in production (the
large portion of the data set in Figure 1),●if
you’re the owner/stockholder of the company in Figure 1 you will see all your problems as low returns on your
investment with expectations that management will make the corrections before
you abandon your investment (or you abandon your management
team for more effective problem resolution) for greener
pastures.
Immediate corrective action is required so you achieve bigger bang for your
money!Action is needed NOW, you can’t not wait forever for
improvements!

What Is The Issue?

Shewhart-the father of statistical process control to reduce variability-In 1931, Walter A. Shewhart of Bell Telephone Laboratories wrote his important
book on the Economic
Control of Quality of Manufactured Product.This book established statistical process control in manufacturing (for
the purpose of economic control of manufacturing by reducing/eliminating
variability).It is still available
after more than 80 years.

In chapter 1 and 2, Shewhart defines two categories of variability:1)Common cause variability
occurs from undefined reasons which we have not yet named and identified for corrective
action.2)Special cause variability occurs from
reasons we can name, we can find the reasons, and we can eliminate the
problems.
Characteristics of common cause and special cause variable are detailed in
Table 1 of the October 2008
problem of the month.

Now here comes the problem:●What if your common cause problems are named with
special cause events that are not real?
When this happens reporters of problems are naming “ghost problems” which are
not in touch with reality.Those
reporting the problem don’t know special cause variability from common cause
variability.The result will be as in
Aesop’s Fable no one will believe the cry of Wolf and
therefore real problems are not solved.Remember the moral of the fable about the Boy Who Cried Wolf :●“Even
when liars tell the truth, they are never believed”.
Therefore problems must be correctly identified for corrective action and you
must sell (not just tell) the solution!

Deming-the
father of involving Japanese management to reduce variability-
In 1982, W. Edwards Deming, the technical guiding light for the Japanese
quality movement, wrote his important book Out
of the Crisis.In chapter 1 Deming
makes this point:“Any substantial
improvement must come from action on the system, the responsibility of
management.”This means management
cannot take a Pontius
Pilate ritual of washing their hands to show they are not responsible for
special causes and common causes—actually, most managers are in over their head
with both special cause and common cause problems which cause financial damage
to their companies (it’s just convenient for them to point fingers to others as
the responsible party).

Management must work to train the
workforce in responsible exception reports.Management must train to identify special cause reasons for
variances and also to lead the workforce in attacking common cause
variability.The “medicines” for solving
these two different problems are different.

Likewise internal consultants such
as six sigma experts and maintenance/reliability experts much also carefully
separate the common cause issues from the special cause issues to make sure the
variance reports are correct.The same
guidelines for correctly categorizing and naming variances apply to outside
consultants for cost effective and timely resolution of problems.

The motivation for reducing/eliminating
special causes is to reduce known problems which reduce productive
output and increase operating costs these issues are part of the hidden factory
robbing profits.The motivation for
reducing/eliminating common causes is to reduce unknown and unidentified
problems that add variability to productive output which increase operating
costs.The common cause problems
do not have names and thus are truly part of the hidden factory; and when the
problems are named, they become special causes.

Misreporting of common cause
variability as special causes has roots in two of the Seven Deadly Sins of
Management which are: ●Failure
to train (train to insure variance
reports are correct) and ●Letting
the job grow like Topsy(train to insure the
workforce reports deviations in a standard format)
Both deadly sins of management require managerial discipline and leadership to
achieve good results.Deming also points
out in Chapter 2: “There is no substitute for teamwork and good leaders of
teams to bring consistency of effort, along with knowledge.”

No medical illness is corrected or
avoided until the illness is properly named—the same is true for
manufacturing!Most manufacturing
processes do not match the requirements for a world class process, thus the
illnesses must be properly named for resolution.Misnaming the illness in manufacturing
dilutes your problem solving efforts with waste and ineffectiveness.

If you have variation reports
quantifying the amount of money lost, this identification is not effective
until the root of the financial variation is named.You must name the problem to focus resources
for resolution.If the problem is
incorrectly or vaguely identified (i.e., naming a common cause problem with an
incorrect name) then effort is wasted.Focus on the illness is mandatory for better financial performance.

In short, Deming requires correctly
identifying the special cause problems with names and the correcting
identifying the common cause problems to convert them into special
causes.In chapter 11 Deming reminds
“The central problem in management and in leadership…is failure to understand
the information in variations.”Deming
claims: ●“..the type of action required to reduce special causes
of variation is totally different from the action required
to reduce variation and faults from the system itself [reduce common
causes of variation] …”.●“We speak of faults of the system as common
causes of trouble, and faults from fleeting events as special
causes.”●“Confusion
between common causes and special causes leads to frustration of everyone, and leads to
greater variability and to higher costs, exactly contrary to what is
needed.”
Deming further explains “…experience shows 94% of troubles belong to the system
which is the responsibility of management [to correct the common cause
variability] and 6% of troubles are due to special causes [which are
easier to correct].”

“We may now formulate two sources of
loss from confusion of special causes with common causes of variation”.1.“Ascribe a variation or a mistake to a special
cause when in fact the cause belongs to the system (common
causes)”.2.“Ascribe a variation or a mistake to the
system (common causes) when in fact the cause was special
[cause]”.
“Over-adjustment is a common example of mistake No. 1”.“Never doing anything to try to find a special
cause is a common example of mistake No. 2”.“The action required to find and eliminate a special
cause is totally different from the action required to improve the process [common
cause].”“Removal of common
causes of trouble and of variation, of errors, of mistakes, of low
production, of low sales, of most accidents is the responsibility of
management.”

How to
categorize correctly common causes and special causes:

Process reliability plots of production outputs from a process give a clear
roadmap of the issues and magnitude of the problem.They are frequently used with exception
reports from the production area.Here
is what you should expect to see for special causes on a Weibull
probability plot for process reliability issues in Figure 2 with all special causes
identified below the cusp.

Figure 2 shows the proper
designation of special cause losses below the point estimate of the production
system reliability point where common causes cease and special cause
begin.Of course you don’t know the
reliability point until all data are in hand.

Figure 2: Special Cause Losses

As a rule of thumb, report all
special causes where production is smaller than the nameplate at 99.9% reliability
as shown in Figure 3—of course if the reliability for the process is poor (say
50% or lower reliability for the process) this rule of thumb is inadequate and
thus choose a point on the nameplate line at a smaller reliability value to
catch the special cause events.The
production line with line slope, b, at
5.5 is truly an inferior process with great variability.Likewise the nameplate line slope, b, at 12.5 is also nothing to write home about
but to the owners of the process the nameplate line (established by judgment)
will look like attempting to climbMt. Everest as the
production line slope improves the nameplate line should be steepened.

By the way, if you are a theoretical
person, you will argue the nameplate line should be vertical, however if you
establish the theoretical vertical line, experience teaches the production team
will consider it impossible and thus they will not rise to the challenge of
improvement—it’s the carrot
or stick challenge.In Figure 3, all
the data points have labels which are a feature of SuperSMITH™ Weibull and Visual software.

Figure 3: Rule Of Thumb For Reporting Variances

If exceptions are reported for the
common cause portion of the line, then retraining is required to correctly
understand variance definitions and what is causing the variance.Otherwise the cry of wolf will occur to
frequently and the variance report will be dismissed as fiction for too many
reports in the common cause section of the curve as it will not represent
reality.

Unfortunately, Figure 4 is a more
typical pattern of reported variances.Notice the larger number of variance reasons and how they are reported
up and down the common cause trendline which is a false explanation of
reasons for the variability.If you can
name the reason for variability, it is NOT common cause variability and thus a
false report in the section of the Weibull plot for common cause
variability.In Figure 4, for SuperSMITH
Weibull version 5.0BI and beyond, each label is turned on individually (go to
the magnifying glass icon, select option for point symbol display, activate the
type of point as label, and then a separate menu appears to select all or by
individual label items with the labeled items appearing one at a time).

Figure 4: Reasons Reported For Common Cause Variability

Generally speaking, the reported
reasons for the common cause variability in Figure 4 are not actionable items
for the reducing common cause variability.The management of this operation are the key
for finding and resolving the common cause variability and it is not an easy
task to correct because the system is not well disciplined.In short, the management of this operation
must put “spit
and polish” into the operation to reduce the variability and this usually
means institutionally the discipline of operating by the numbers/procedures to
avoid the high variability of a Keystone Kops effort of
being extremely busy but not productive.

Other Process Reliability References:You can download other articles from this site concerning process
reliability:

Refer to the caveats on theProblem Of The Month Pageabout the
limitations of the above solution.Maybe
you have a better idea on how to solve the problem.Maybe you will find that I’ve screwed-up the
solution and you can point out my errors as you check my calculations.E-mail your comments, criticism, and
corrections toPaul
Barringer.